Possible decline of the urban forest in San Jose, California as seen through change in vegetation greenness¶

San Jose, California skyline with urban forest in the foreground

Image source: Ben Loomis, CC BY 2.0, via Wikimedia Commons..jpg)

'/home/jovyan/earth-analytics/data/camp-fire'

Map of city limits¶

OBJECTID FACILITYID INTID INCORPORATED INCORPAREA LASTUPDATE NOTES SHAPE_Length SHAPE_Area geometry
0 1 1 1 San Jose 180.75 1688752509000 1.429474e+06 5.039148e+09 MULTIPOLYGON (((-121.78237 37.26501, -121.7823...

Data Citation

City Limits. (n.d.). City of San Jose, California Open GIS Portal. Retrieved October 3, 2023, from https://gisdata-csj.opendata.arcgis.com/datasets/city-limits/explore?location=37.301433,-121.823480,11.00

Make this Notebook Trusted to load map: File -> Trust Notebook

I downloaded NDVI data from NASA Appeears for the city of San Jose city limit. I only used data from the month of August, when the wild grasses growing in open spaces within the city are senesced, to capture changes in greenness in primarily human-planted and managed vegetation.

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 Coordinates:
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 Coordinates:
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<xarray.Dataset>
Dimensions:      (x: 222, y: 168, date: 33)
Coordinates:
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    spatial_ref  int64 0
  * date         (date) datetime64[ns] 2012-07-19 2012-08-04 ... 2022-08-21
Data variables:
    NDVI         (date, y, x) float32 nan nan nan nan ... 0.4652 0.348 0.2779
xarray.Dataset
    • x: 222
    • y: 168
    • date: 33
    • band
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      int64
      1
      array(1)
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      float64
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    • spatial_ref
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      semi_major_axis :
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      semi_minor_axis :
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      inverse_flattening :
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      reference_ellipsoid_name :
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      longitude_of_prime_meridian :
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      prime_meridian_name :
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      geographic_crs_name :
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      horizontal_datum_name :
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      grid_mapping_name :
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      spatial_ref :
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      GeoTransform :
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      array(0)
    • date
      (date)
      datetime64[ns]
      2012-07-19 ... 2022-08-21
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       '2014-08-21T00:00:00.000000000', '2015-07-20T00:00:00.000000000',
       '2015-08-05T00:00:00.000000000', '2015-08-21T00:00:00.000000000',
       '2016-07-19T00:00:00.000000000', '2016-08-04T00:00:00.000000000',
       '2016-08-20T00:00:00.000000000', '2017-07-20T00:00:00.000000000',
       '2017-08-05T00:00:00.000000000', '2017-08-21T00:00:00.000000000',
       '2018-07-20T00:00:00.000000000', '2018-08-05T00:00:00.000000000',
       '2018-08-21T00:00:00.000000000', '2019-07-20T00:00:00.000000000',
       '2019-08-05T00:00:00.000000000', '2019-08-21T00:00:00.000000000',
       '2020-07-19T00:00:00.000000000', '2020-08-04T00:00:00.000000000',
       '2020-08-20T00:00:00.000000000', '2021-07-20T00:00:00.000000000',
       '2021-08-05T00:00:00.000000000', '2021-08-21T00:00:00.000000000',
       '2022-07-20T00:00:00.000000000', '2022-08-05T00:00:00.000000000',
       '2022-08-21T00:00:00.000000000'], dtype='datetime64[ns]')
    • NDVI
      (date, y, x)
      float32
      nan nan nan ... 0.4652 0.348 0.2779
      array([[[   nan,    nan,    nan, ..., 0.3766, 0.3726, 0.3726],
        [   nan,    nan,    nan, ..., 0.4266, 0.4266, 0.4219],
        [   nan,    nan,    nan, ..., 0.4072, 0.4762, 0.4762],
        ...,
        [0.8713, 0.8772, 0.8718, ..., 0.3955, 0.3683, 0.3683],
        [0.871 , 0.8761, 0.8761, ..., 0.5501, 0.4478, 0.3096],
        [0.8014, 0.8014, 0.8078, ..., 0.5218, 0.5218, 0.2917]],

       [[   nan,    nan,    nan, ..., 0.3867, 0.3659, 0.3659],
        [   nan,    nan,    nan, ..., 0.4625, 0.4625, 0.4221],
        [   nan,    nan,    nan, ..., 0.4096, 0.4625, 0.4319],
        ...,
        [0.8576, 0.8827, 0.8741, ..., 0.3692, 0.3687, 0.3687],
        [0.8672, 0.8499, 0.8499, ..., 0.496 , 0.496 , 0.3266],
        [0.7925, 0.7925, 0.7951, ..., 0.4843, 0.4767, 0.3112]],

       [[   nan,    nan,    nan, ..., 0.3369, 0.3518, 0.3518],
        [   nan,    nan,    nan, ..., 0.3786, 0.3786, 0.4249],
        [   nan,    nan,    nan, ..., 0.3738, 0.4193, 0.4315],
        ...,
...
        [0.8531, 0.8489, 0.8434, ..., 0.3663, 0.2991, 0.2991],
        [0.8085, 0.8147, 0.8147, ..., 0.2476, 0.2998, 0.3188],
        [0.7699, 0.7699, 0.7664, ..., 0.2844, 0.338 , 0.3188]],

       [[   nan,    nan,    nan, ..., 0.3926, 0.3472, 0.3472],
        [   nan,    nan,    nan, ..., 0.4471, 0.4471, 0.435 ],
        [   nan,    nan,    nan, ..., 0.4031, 0.4649, 0.4222],
        ...,
        [0.8497, 0.8483, 0.8371, ..., 0.3617, 0.3648, 0.3648],
        [0.7896, 0.7881, 0.7881, ..., 0.2541, 0.2738, 0.3131],
        [0.7692, 0.7692, 0.7857, ..., 0.3667, 0.3333, 0.3333]],

       [[   nan,    nan,    nan, ..., 0.3372, 0.3384, 0.3384],
        [   nan,    nan,    nan, ..., 0.3825, 0.3825, 0.359 ],
        [   nan,    nan,    nan, ..., 0.3451, 0.3931, 0.4064],
        ...,
        [0.8321, 0.8371, 0.8284, ..., 0.3152, 0.3284, 0.3284],
        [0.8218, 0.7591, 0.7591, ..., 0.2203, 0.3055, 0.3058],
        [0.7561, 0.7561, 0.8087, ..., 0.4652, 0.348 , 0.2779]]],
      dtype=float32)
    • x
      PandasIndex
      PandasIndex(Index([ -122.0468749890664, -122.04479165573325,  -122.0427083224001,
       -122.04062498906696, -122.03854165573381, -122.03645832240066,
       -122.03437498906752, -122.03229165573437, -122.03020832240122,
       -122.02812498906808,
       ...
        -121.6052083224393, -121.60312498910615,   -121.601041655773,
       -121.59895832243986, -121.59687498910671, -121.59479165577356,
       -121.59270832244042, -121.59062498910727, -121.58854165577412,
       -121.58645832244098],
      dtype='float64', name='x', length=222))
    • y
      PandasIndex
      PandasIndex(Index([ 37.46979166330993,  37.46770832997678,  37.46562499664363,
        37.46354166331049,  37.46145832997734,  37.45937499664419,
        37.45729166331105,   37.4552083299779,  37.45312499664475,
        37.45104166331161,
       ...
        37.14062499667275,   37.1385416633396, 37.136458330006455,
        37.13437499667331,  37.13229166334016, 37.130208330007015,
        37.12812499667387,  37.12604166334072, 37.123958330007575,
        37.12187499667443],
      dtype='float64', name='y', length=168))
    • date
      PandasIndex
      PandasIndex(DatetimeIndex(['2012-07-19', '2012-08-04', '2012-08-20', '2013-07-20',
               '2013-08-05', '2013-08-21', '2014-07-20', '2014-08-05',
               '2014-08-21', '2015-07-20', '2015-08-05', '2015-08-21',
               '2016-07-19', '2016-08-04', '2016-08-20', '2017-07-20',
               '2017-08-05', '2017-08-21', '2018-07-20', '2018-08-05',
               '2018-08-21', '2019-07-20', '2019-08-05', '2019-08-21',
               '2020-07-19', '2020-08-04', '2020-08-20', '2021-07-20',
               '2021-08-05', '2021-08-21', '2022-07-20', '2022-08-05',
               '2022-08-21'],
              dtype='datetime64[ns]', name='date', freq=None))

NDVI should take values between -1.0 and 1.0. Let's make sure that is the case here.

(array([  1000.,   1538.,  16312., 125741., 393000., 257370., 161454.,
        119786.,  94390.,  31379.]),
 array([-0.19939999, -0.08719999,  0.025     ,  0.1372    ,  0.24939999,
         0.36159998,  0.47379997,  0.58599997,  0.69819999,  0.81039995,
         0.92259997]),
 <BarContainer object of 10 artists>)
No description has been provided for this image

A city report mentions that the urban forest canopy cover in San Jose has declined by 1.82% from 2012 to 2018. Let's see if NDVI within the city limits changed from 2012 to 2022.

EPSG:4326
EPSG:4326

<Axes: title={'center': 'band = 1, spatial_ref = 0'}, xlabel='x', ylabel='y'>
No description has been provided for this image
No description has been provided for this image No description has been provided for this image

Did the NDVI drop more inside the city limits than nearby areas?¶

geometry
0 MULTIPOLYGON (((-122.04607 37.12272, -122.0460... 
(          NDVI
 year          
 2012  0.323291
 2013  0.330175
 2014  0.310622
 2015  0.306544
 2016  0.313752
 2017  0.319050
 2018  0.311588
 2019  0.330538
 2020  0.316916
 2021  0.304515
 2022  0.299192,
           NDVI
 year          
 2012  0.477988
 2013  0.480093
 2014  0.460221
 2015  0.472924
 2016  0.476541
 2017  0.474574
 2018  0.474207
 2019  0.503266
 2020  0.469537
 2021  0.440626
 2022  0.444641)

How did August NDVI in San Jose change from 2012 to 2022?¶

The difference in NDVI between inside and outside city limits increased over time

This could suggest that the urban greenness of San Jose is decling faster than surrounding cities. However, this analysis does not differentiate between adjacent city areas and adjacent areas of naturally existing forest, which are likely to exhibit different NDVI patterns. Therefore, it is not entirely possible to know from this analysis how the urban greenness of San Jose is changing over time compared to other nearby cities.